CS Seminar (Data Centric): Cristina Muntean (High Performance Computing Lab (HPC); Information Retrieval & Machine Learning ; ISTI-CNR, Italy)
Effectiveness and Efficiency Advancements in Conversational Search
Abstract: In a conversational context, a user converses with a system through a sequence of natural-language questions, i.e., utterances. Starting from a given subject, the conversation evolves through sequences of user utterances and system replies. We aim at improving both the quality (effectiveness) of the replies and the processing time (efficiency) required to search those replies. We address the quality aspect by proposing an adaptive utterance rewriting strategy based on the current utterance and the dialogue evolution of the user with the system. Retrieving relevant documents for a question is difficult due to the informal use of natural language in speech and the complexity of understanding the semantic context coming from previous questions/utterances. In our system, a classifier identifies utterances lacking context information as well as the dependencies on the previous utterances. Our modular architecture performs: (i) automatic utterance understanding and rewriting, (ii) first-stage retrieval of candidate passages for the rewritten utterances, and (iii) neural re-ranking of candidate passages to retrieve the most relevant documents as replies. Rapid response is fundamental in search applications; it is particularly so in interactive search sessions, such as those encountered in conversational settings. To address the efficiency aspect, we exploit the temporal locality of conversational queries and propose and evaluate a client-side document embedding cache to improve responsiveness. By leveraging state-of-the-art dense retrieval models to abstract document and query semantics, we cache the embeddings of documents retrieved for a topic introduced in the conversation, as they are likely relevant to successive queries. Our document embedding cache implements an efficient metric index answering nearest-neighbor similarity queries by estimating the approximate result sets returned. It significantly improves the responsiveness of conversational systems while likewise reducing the number of queries managed on the search back-end.
Bio: Cristina-Ioana Muntean is a researcher at the HPC lab of ISTI-CNR, Pisa (Italy). Her main research interests are in Information Retrieval and Machine Learning with applications to web search and social media. She is particularly interested in passage retrieval and conversational search using neural and classic IR models. She is interested in both effectiveness and efficiency issues regarding conversational search. She is an active member of the SIGIR, ECIR, CIKM, WSDM, and The Web Conference communities, publishing and serving as PC member.